9 papers
Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality
Danfeng Hong, Chenyu Li, Xuyang Li +2
Remote sensing (RS) techniques are increasingly crucial for deepening our understanding of the planet. As the volume and diversity of RS data continue to grow exponentially, there…
Hyperspectral Imaging
Danfeng Hong, Chenyu Li, Naoto Yokoya +6
Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, c…
KANO: Kolmogorov-Arnold Neural Operator for Image Super-Resolution
Chenyu Li, Danfeng Hong, Bing Zhang +2
The highly nonlinear degradation process, complex physical interactions, and various sources of uncertainty render single-image Super-resolution (SR) a particularly challenging tas…
MambaX: Image Super-Resolution with State Predictive Control
Chenyu Li, Danfeng Hong, Bing Zhang +3
Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainly focus on directly enhancing th…
SpectralEarth: Training Hyperspectral Foundation Models at Scale
Nassim Ait Ali Braham, Conrad M Albrecht, Julien Mairal +3
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potent…
UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
Chenyu Li, Danfeng Hong, Bing Zhang +4
Object extraction and segmentation from remote sensing (RS) images is a critical yet challenging task in urban environment monitoring. Urban morphology is inherently complex, with…